Now we know. Cross a genetic algorithm with your favorite toy from childhood (Lego!) and you get intelligent, biologically reminiscent structures. Dr. Pablo Funes and his team at the Dynamical & Evolutionary Machine Organization devised a very cool simulator that can be told to create Lego structures of various kinds, such as bridges, using evolutionary algorithms. The creative aspect provides interesting food for thought: the system is given a goal and the solution design is entirely dependant on the machine.

The simulation takes physics into account: each structure is evaluated depending on its joints and external (gravitational) forces. The propagated force of the entire structure is then used to determine whether the load is too much — whether the system needs to add or remove bricks somewhere to avoid collapsing (“dying”).

The Lego brick structures are represented as trees in the simulator, and it’s to this structure that genetic operators are applied: Mutations modify a brick’s size or position and recombinations interchange subtrees of root-bricks.

The system can thus be given a goal, such as building a bridge over a large gap; you can see an animated gif of this process — where they gave it the goal of bridging a gap between two tables in their lab. The end results are quite interesting to the eye, in two words I’d describe them as “naturally messy”.

The crane’s evolved design is noteworthy. To counteract the load posed on the crane-arm itself, the structure evolve a J-shaped crown that extended from its back, as evident on the picture above. Picture (c) shows the intermediate stages of the crane, picture (d) shows the crane in its final stages — where the counter-balancing structure evolved a solid connection with the base. The below diagram shows the design of the crane in three stages (top is first stage, bottom final stage).

It’s interesting how genetic algorithms result in, like I said before, naturally messy constructs. This is actually one of the reasons that genetic algorithms are popular within artificial creativity research. Evolution results in various “unanticipated” or “surprising” constructs which can be perceived as creative. The problem with this approach to creativity presents itself if you want a creative machine that creates less messy structures. You’d invevitably have to add additional constraints which would in turn minimize the surprising factor of the solution — voiding it of “creativity”. Quite an interesting dilemma.